CN116610983A - Abnormality analysis method and system for air purification control system - Google Patents
Abnormality analysis method and system for air purification control system Download PDFInfo
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Abstract
The invention provides an anomaly analysis method and an anomaly analysis system for an air purification control system, and relates to the technical field of artificial intelligence and air purification. The invention determines a typical air purifying device in typical air purifying devices included in a typical purifying device cluster and analyzes a corresponding data mining mode; performing data feature mining operation on the device operation description data based on a data mining mode to output device operation feature representation; performing network optimization operation on the initial anomaly analysis network through device operation feature representation to output a target anomaly analysis network; and performing an anomaly analysis operation on the target air purification device through the target anomaly analysis network to output anomaly analysis result data corresponding to the target air purification device. According to the invention, the air purification control system in the air purification device is analyzed, so that the reliability of anomaly analysis is improved, and the air purification efficiency of the air purification device is improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence and air purification, in particular to an anomaly analysis method and an anomaly analysis system of an air purification control system.
Background
Artificial intelligence (Artificial Intelligence, AI for short) is a theory, method, technique and application system that simulates, extends and extends human intelligence, senses environment, obtains knowledge and uses knowledge to obtain optimal results using digital computers or digital computer controlled computations. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision. The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, electromechanical integration, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning, automatic driving, intelligent traffic and other directions. Among them, machine Learning (ML) is a multi-domain interdisciplinary, and involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. Machine learning is the core of artificial intelligence, a fundamental approach to letting computers have intelligence, which is applied throughout various areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.
However, in the prior art, there is a problem of low reliability in the process of performing anomaly analysis and evaluation.
Disclosure of Invention
Accordingly, an object of the present invention is to provide an abnormality analysis method and system for an air purification control system, which can improve the reliability of the abnormality analysis to a certain extent.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
an anomaly analysis method of an air purification control system, comprising:
performing a lottery operation on typical air cleaning devices included in the typical cleaning device cluster according to at least one lottery rule to determine a lottery typical air cleaning device matching a lottery state of the target device;
sampling operation is carried out on the sampling typical air purifying device through grouping evaluation operation, so that sampling typical air purifying devices corresponding to grouping evaluation operation output data matched with a target running state are output;
determining device operation description data of the sampling typical air purification device, and analyzing a corresponding data mining mode based on data type information of the device operation description data, wherein the device operation description data belongs to text data;
Performing data characteristic mining operation on the device operation description data based on the data mining mode to output device operation characteristic representations corresponding to the sampling typical air purifying device;
performing network optimization operation on an initial anomaly analysis network through the device operation characteristic representation to output a target anomaly analysis network corresponding to the initial anomaly analysis network;
determining the residual using time of a filter device in a target air purifying device based on the current power of a fan in the target air purifying device, determining the size relation between the residual using time and a preset time, and carrying out an abnormality analysis operation on the target air purifying device through the target abnormality analysis network under the condition that the residual using time is less than or equal to the preset time so as to output abnormality analysis result data corresponding to the target air purifying device, wherein the abnormality analysis result data is used for reflecting the abnormal operation type of the target air purifying device.
In some preferred embodiments, in the abnormality analysis method of an air cleaning control system, the step of performing a lottery operation on typical air cleaning devices included in a typical cleaning device cluster according to at least one lottery rule to determine a lottery typical air cleaning device matching a lottery state of a target device includes:
Marking a reference typical air purification device in the typical air purification devices included in the typical purification device cluster, wherein the reference typical air purification device is provided with a corresponding manual operation identifier;
performing a device-associated replenishment operation based on the reference typical air cleaning device such that, in the typical cleaning device cluster, a replenishment typical air cleaning device associated with the reference typical air cleaning device is decimated;
the reference representative air cleaning device and the supplemental representative air cleaning device are marked to mark as a lottery representative air cleaning device matching a lottery status of the target device.
In some preferred embodiments, in the abnormality analysis method of the above-described air cleaning control system, the supplementary typical air cleaning device belongs to a combination result of the first typical air cleaning device and the second typical air cleaning device;
the step of performing a device-associated replenishment operation based on the reference typical air cleaning device such that, in the typical cleaning device cluster, a replenishment typical air cleaning device associated with the reference typical air cleaning device is decimated, comprising:
performing a contrast analysis operation on a specified typical air cleaning device to output a first typical air cleaning device associated with the reference typical air cleaning device among the specified typical air cleaning devices including a portion of the reference typical air cleaning device and typical air cleaning devices other than the reference typical air cleaning device in the typical cleaning device cluster by an optimized contrast analysis model formed by performing a network optimization operation on a portion of the reference typical air cleaning device as relevant typical data and the specified typical air cleaning device as irrelevant typical data;
Performing a related device supplementing operation according to the reference typical air purifying device to determine a second typical air purifying device associated with the reference typical air purifying device in the typical air purifying devices included in the typical air purifying device cluster.
In some preferred embodiments, in the abnormality analysis method of an air purification control system described above, the abnormality analysis method of an air purification control system further includes:
performing a device analysis operation on candidate typical air cleaning devices through a device association analysis model to analyze a third typical air cleaning device associated with the reference typical air cleaning device among the candidate typical air cleaning devices, the candidate typical air cleaning devices including typical air cleaning devices other than the reference typical air cleaning device in the typical air cleaning device cluster;
classifying the candidate typical air cleaning devices so as to analyze a fourth typical air cleaning device associated with the reference typical air cleaning device among the candidate typical air cleaning devices;
the reference typical air cleaning device, the supplementary typical air cleaning device, the third typical air cleaning device, and the fourth typical air cleaning device are combined to form a lottery typical air cleaning device matching a lottery state of a target device.
In some preferred embodiments, in the abnormality analysis method of an air cleaning control system described above, the step of determining device operation description data of the sampled typical air cleaning device includes:
determining historical device description information of the sampling typical air purification device;
and according to the historical device description information, performing key information screening operation on the sampling typical air purification device to obtain device operation description data comprising description data of device key information, description data of user key information and description data of operation key information, wherein the device key information is used for representing attribute information of the sampling typical air purification device, the user key information is used for representing attribute information of a user used by a device corresponding to the sampling typical air purification device, and the operation key information is used for representing operation process information of the sampling typical air purification device.
In some preferred embodiments, in the method for analyzing an abnormality of an air purification control system, the step of performing a data feature mining operation on the device operation description data based on the data mining manner to output a device operation feature representation corresponding to the sampled typical air purification device includes:
Analyzing a data mining mode corresponding to data type information of each local device operation description data in the device operation description data;
according to the data mining mode corresponding to each local device operation description data, carrying out data characteristic mining operation on each local device operation description data so as to mine out a local device operation characteristic representation corresponding to each local device operation description data;
and performing aggregation operation of the characteristic representation according to the local device operation characteristic representation to determine the device operation characteristic representation corresponding to the sampling typical air purification device, wherein the aggregation operation of the characteristic representation at least comprises cascade combination operation of the local device operation characteristic representation.
In some preferred embodiments, in the abnormality analysis method of the air purification control system described above, the partial device operation characteristic representation mined includes a first partial device operation characteristic representation and a second partial device operation characteristic representation;
the step of performing data feature mining operation on each local device operation description data according to a data mining mode corresponding to each local device operation description data so as to mine out a local device operation feature representation corresponding to each local device operation description data includes:
Performing a comparative analysis operation on each of the local device operation description data to form local device operation description data satisfying the abnormal state analysis and local device operation description data not satisfying the abnormal state analysis;
performing data feature mining operation on the local device operation description data meeting the abnormal state analysis according to a corresponding data mining mode to output a first local device operation feature representation corresponding to the local device operation description data meeting the abnormal state analysis;
and performing abnormal data embedding operation on the local device operation description data which does not meet the abnormal state analysis, and marking the output abnormal data embedding characteristic representation as a second local device operation characteristic representation corresponding to the local device operation description data which does not meet the abnormal state analysis.
In some preferred embodiments, in the abnormality analysis method of an air purification control system described above, the abnormality analysis method of an air purification control system further includes:
for two target air cleaning devices having correlation, the first target air cleaning device and the second target air cleaning device are respectively marked;
Acquiring device operation description data of the first target air purification device to form first device operation description data, and acquiring device operation description data of the second target air purification device to form second device operation description data;
mining description data characteristic representations corresponding to the first device operation description data and the second device operation description data respectively, wherein the description data characteristic representations are used for reflecting each description data segment in the corresponding device operation description data and the distribution coordinates of each description data segment in the corresponding device operation description data;
performing focusing characteristic analysis strengthening operation of the sub-channels on each description data characteristic representation respectively so as to output strengthening data characteristic representations corresponding to each description data characteristic representation respectively;
performing associated focusing characteristic analysis operation on each enhanced data characteristic representation to form a first data focusing characteristic representation corresponding to the first device operation description data and a second data focusing characteristic representation corresponding to the second device operation description data;
performing data correlation analysis operation on the first device operation description data and the second device operation description data according to the first data focusing characteristic representation and the second data focusing characteristic representation so as to output corresponding data correlation characterization parameters;
And based on the data correlation characterization parameters, performing reliability analysis operation on the abnormal analysis result data corresponding to the two target air purification devices with correlation to output reliability analysis results corresponding to the two target air purification devices with correlation, wherein the reliability analysis results are used for reflecting the reliability degree of the abnormal analysis result data corresponding to the two target air purification devices with correlation.
In some preferred embodiments, in the abnormality analysis method of an air purification control system, the step of mining out the description data characteristic representations respectively corresponding to the first device operation description data and the second device operation description data includes:
mining primary data characteristic representations corresponding to the first device operation description data and the second device operation description data respectively; for each primary data characteristic representation, analyzing a distribution coordinate characteristic representation corresponding to the distribution coordinates of the target data units in each description data segment according to each description data segment corresponding to the primary data characteristic representation; and merging the distributed coordinate feature representations into the corresponding primary data feature representations to form description data feature representations respectively corresponding to the first device operation description data and the second device operation description data;
And the step of performing focus feature analysis enhancement operation of the channels on each of the description data feature representations to output enhancement data feature representations corresponding to each of the description data feature representations, includes:
for each device operation description data, determining a description data characteristic representation corresponding to the device operation description data and a channel local characteristic representation associated with the description data characteristic representation; and performing multiplication operation on each description data characteristic representation and the associated channel local characteristic representation to output an enhanced data characteristic representation corresponding to each device operation description data;
and performing a focus characteristic analysis operation associated with each of the enhanced data characteristic representations to form a first data focus characteristic representation corresponding to the first device operation description data and a second data focus characteristic representation corresponding to the second device operation description data, including:
performing cascading combination operation on the reinforced data characteristic representation corresponding to the first device operation description data and the reinforced data characteristic representation corresponding to the second device operation description data to form a corresponding cascading reinforced data characteristic representation; performing focusing characteristic analysis operation on the cascade enhancement data characteristic representation to output a corresponding focusing enhancement data characteristic representation; and performing a decomposition operation of the feature representation on the focus reinforcement data feature representation according to a cascade combination principle of the cascade reinforcement data feature representation to form a first data focus feature representation corresponding to the first device operation description data and a second data focus feature representation corresponding to the second device operation description data.
The embodiment of the invention also provides an abnormality analysis system of the air purification control system, which comprises a processor and a memory, wherein the memory is used for storing a computer program, and the processor is used for executing the computer program so as to realize the abnormality analysis method of the air purification control system.
The abnormality analysis method and system of the air purification control system provided by the embodiment of the invention can determine the typical air purification device selected from the typical air purification devices included in the typical purification device cluster; determining a sampling typical air purifying device; determining device operation description data of a sampling typical air purifying device, and analyzing a corresponding data mining mode; performing data feature mining operation on the device operation description data based on a data mining mode to output device operation feature representation; performing network optimization operation on the initial anomaly analysis network through device operation feature representation to output a target anomaly analysis network; and performing an anomaly analysis operation on the target air purification device through the target anomaly analysis network to output anomaly analysis result data corresponding to the target air purification device. Based on the foregoing, the device operation characteristics for performing the network optimization operation indicate that the corresponding device operation description data is obtained through two times of screening, so that the basis of the network optimization operation is more reliable, and therefore, the analysis precision of the target anomaly analysis network formed by optimization can be ensured, the reliability of the anomaly analysis result data analyzed based on the target anomaly analysis network is ensured, that is, the reliability of anomaly analysis is improved to a certain extent, and the problem of low reliability in the prior art is solved.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of an abnormality analysis system of an air purification control system according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of steps included in an abnormality analysis method of an air purification control system according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of each module included in the abnormality analysis device of the air purification control system according to the embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, an embodiment of the present invention provides an abnormality analysis system of an air cleaning control system. Wherein the anomaly analysis system may include a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize transmission or interaction of data. For example, electrical connection may be made to each other via one or more communication buses or signal lines. The memory may store at least one software functional module (computer program) that may exist in the form of software or firmware. The processor may be configured to execute an executable computer program stored in the memory, thereby implementing the abnormality analysis method of the air purification control system provided by the embodiment of the present invention.
It should be appreciated that in some possible embodiments, the Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.
It should be appreciated that in some possible embodiments, the processor may be a general purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a System on Chip (SoC), etc.; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
It should be appreciated that in some possible embodiments, the anomaly analysis system of the air purification control system may be a server with data processing capabilities.
Referring to fig. 2, an embodiment of the present invention further provides an anomaly analysis method for an air purification control system, which is applicable to the anomaly analysis system of the air purification control system. The method steps defined by the flow related to the abnormality analysis method of the air purification control system can be realized by the abnormality analysis system of the air purification control system.
The specific flow shown in fig. 2 will be described in detail.
Step S110, performing a lottery operation on the typical air cleaning devices included in the typical cleaning device cluster according to at least one lottery rule to determine a lottery typical air cleaning device matching the lottery status of the target device.
In the embodiment of the invention, the abnormality analysis system of the air purification control system may perform a decimation operation on the typical air purification devices included in the typical purification device cluster according to at least one decimation rule to determine a decimated typical air purification device matching with the decimation state of the target device. The air purification device is a part of an air purification control system.
Step S120, sampling operation is carried out on the sampling typical air purifying device through grouping evaluation operation, so as to output sampling typical air purifying device corresponding to grouping evaluation operation output data matched with the target running state.
In the embodiment of the invention, the abnormality analysis system of the air purification control system can perform sampling operation on the sampling typical air purification device through grouping evaluation operation so as to output the sampling typical air purification device corresponding to grouping evaluation operation output data matched with the target running state.
Step S130, determining device operation description data of the sampling typical air purification device, and analyzing a corresponding data mining mode based on data type information of the device operation description data.
In the embodiment of the invention, the abnormality analysis system of the air purification control system can determine the device operation description data of the sampling typical air purification device, and analyze the corresponding data mining mode based on the data type information of the device operation description data. The device operation description data belongs to text data, namely, the description of related information of the device is carried out in a text mode.
And step S140, performing data characteristic mining operation on the device operation description data based on the data mining mode so as to output device operation characteristic representations corresponding to the sampling typical air purifying device.
In the embodiment of the invention, the abnormality analysis system of the air purification control system can perform data feature mining operation on the device operation description data based on the data mining mode to output device operation feature representations corresponding to the sampling typical air purification device, namely different data mining modes are adopted to ensure the accuracy of the data feature mining operation.
And step S150, performing network optimization operation on the initial anomaly analysis network through the device operation characteristic representation so as to output a target anomaly analysis network corresponding to the initial anomaly analysis network.
In the embodiment of the invention, the anomaly analysis system of the air purification control system may perform network optimization operation on the initial anomaly analysis network through the device operation feature representation to output a target anomaly analysis network corresponding to the initial anomaly analysis network, for example, may perform anomaly analysis operation based on the device operation feature representation to obtain corresponding anomaly analysis result data, then may perform comparison analysis on the anomaly analysis result data and configured actual anomaly characterization data to determine corresponding error parameters, and then perform optimization adjustment on network parameters of the initial anomaly analysis network based on the error parameters to form the target anomaly analysis network.
Step S160, determining a remaining usage time of the filtering device in the target air purifying device based on a current power of the fan in the target air purifying device, determining a size relationship between the remaining usage time and a preset time, and performing an anomaly analysis operation on the target air purifying device through the target anomaly analysis network when the remaining usage time is less than or equal to the preset time, so as to output anomaly analysis result data corresponding to the target air purifying device.
In the embodiment of the invention, the abnormality analysis system of the air purification control system may determine a remaining usage period of a filter device in a target air purification device based on a current power of a fan in the target air purification device, determine a size relationship between the remaining usage period and a preset period, and perform, when the remaining usage period is less than or equal to the preset period, an abnormality analysis operation on the target air purification device through the target abnormality analysis network, so as to output abnormality analysis result data corresponding to the target air purification device. The abnormality analysis result data is used to reflect an operation abnormality type of the target air cleaning device. In addition, the specific value of the preset duration is not limited, and the configuration can be carried out according to actual requirements. The current power of a fan in the air purifying device can be acquired firstly; then, determining the actual air inlet quantity of the air purifying device according to the current power; further, the remaining use time of the filtering device in the air purifying device can be determined according to the actual air inlet quantity. Therefore, the actual air inlet quantity is determined through the acquired power, and the residual use time of the air purifying device is further determined through the actual air inlet quantity, so that the residual use time of the air purifying device is obtained, other devices or equipment are not required to be deployed, the cost can be lower, and the application effect is better.
Based on the foregoing, the device operation characteristics for performing the network optimization operation indicate that the corresponding device operation description data is obtained through two times of screening, so that the basis of the network optimization operation is more reliable, and therefore, the analysis precision of the target anomaly analysis network formed by optimization can be ensured, the reliability of the anomaly analysis result data analyzed based on the target anomaly analysis network is ensured, that is, the reliability of anomaly analysis is improved to a certain extent, and the problem of low reliability in the prior art is solved.
It should be understood that, in some possible embodiments, the step S110 may further include the following specific implementations:
in the typical air purifying devices included in the typical purifying device cluster, a reference typical air purifying device is marked, and the reference typical air purifying device has corresponding manual operation identification, that is, the reference typical air purifying device can be manually made;
performing a device-associated replenishment operation based on the reference typical air cleaning device such that, in the typical cleaning device cluster, a replenishment typical air cleaning device associated with the reference typical air cleaning device is decimated;
The reference representative air cleaning device and the supplementary representative air cleaning device are marked to be a lottery representative air cleaning device matching a lottery state of the target device, i.e., the reference representative air cleaning device and the corresponding supplementary representative air cleaning device are both taken as a lottery representative air cleaning device matching a lottery state of the target device.
It should be appreciated that in some possible embodiments, the supplemental exemplary air purification apparatus belongs to a combination of a first exemplary air purification apparatus and a second exemplary air purification apparatus, based on which the performing of apparatus-related supplemental operations based on the reference exemplary air purification apparatus, such that the step of extracting the supplemental exemplary air purification apparatus associated with the reference exemplary air purification apparatus in the exemplary purification apparatus cluster, may further include the following specific implementations:
performing a comparative analysis operation on a specified typical air cleaning device including a part of the reference typical air cleaning device and typical air cleaning devices other than the reference typical air cleaning device in the typical cleaning device cluster by an optimized comparative analysis model formed by performing a network optimization operation on a part of the reference typical air cleaning device as relevant typical data and the specified typical air cleaning device as irrelevant typical data to output a first typical air cleaning device associated with the reference typical air cleaning device among the specified typical air cleaning devices, for example, classifying or clustering the typical air cleaning devices by the optimized comparative analysis model, and then, as an associated first typical air cleaning device, a typical air cleaning device belonging to the same class as the reference typical air cleaning device among the specified typical air cleaning devices;
Performing a related device supplementing operation according to the reference typical air cleaning device to determine a second typical air cleaning device associated with the reference typical air cleaning device from among the typical air cleaning devices included in the typical air cleaning device cluster, for example, a typical air cleaning device similar to an application scenario or an application environment between the reference typical air cleaning devices may be determined as the second typical air cleaning device associated with the reference typical air cleaning device.
It should be understood that, in some possible embodiments, the method for analyzing an abnormality of the air purification control system may further include the following specific implementation matters:
performing a device analysis operation on candidate typical air cleaning devices to analyze a third typical air cleaning device associated with the reference typical air cleaning device among the candidate typical air cleaning devices including typical air cleaning devices other than the reference typical air cleaning device in the typical air cleaning device cluster by means of a device association analysis model, for example, performing type recognition (the basis of the type recognition may be operation data of the corresponding device) on the typical air cleaning devices by means of the device association analysis model to output corresponding recognition type data, and then, candidate typical air cleaning devices consistent with or similar to the recognition type data of the reference typical air cleaning device may be used as the third typical air cleaning device;
Performing a classification operation on the candidate typical air-cleaning devices so as to analyze a fourth typical air-cleaning device associated with the reference typical air-cleaning device among the candidate typical air-cleaning devices, for example, a similarity between operation data corresponding to the typical air-cleaning devices may be calculated, and then, each typical air-cleaning device may be classified (a large similarity is assigned to one classification set) based on the similarity, so that the candidate typical air-cleaning device belonging to one classification set with the reference typical air-cleaning device may be determined as the fourth typical air-cleaning device;
the reference typical air cleaning device, the supplementary typical air cleaning device, the third typical air cleaning device, and the fourth typical air cleaning device are combined to form a lottery typical air cleaning device matching a lottery state of a target device.
It should be understood, however, that in some possible embodiments, the step S120 may further include the following specific implementations:
performing a sorting and combining operation on the drawn typical air cleaning devices, and configuring corresponding comparison typical device combinations for each typical device combination formed by the sorting and combining to obtain combination corresponding data, for example, 30 drawn typical air cleaning devices can be divided into 6 typical device combinations, each typical device combination comprises at least one drawn typical air cleaning device, and the number of the sorting and combining can be configured according to actual requirements; after sorting the combinations, a corresponding comparison representative device combination may be configured for each representative device combination, e.g., a comparison representative device combination x1 may be configured for representative device combination x, such that x and x1 form a combination correspondence data;
Marking each combination corresponding data respectively to mark a manual auditing object, performing object allocation operation based on the number of units of a target checking unit, and outputting object allocation operation output data of each combination corresponding data in the target checking unit, wherein the object allocation operation output data among the target checking units has a target cross relation; for example, the number of units of the target inspection units may be equal to 2, each target inspection unit performs a comparison test, for example, the lottery typical air purification device in the typical device combination is divided into at least one sub-typical device combination for the 1 st target inspection unit and at least one sub-typical device combination for the 2 nd target inspection unit, and likewise, the typical air purification device in the comparison typical device combination may be divided into at least one sub-comparison typical device combination for each target inspection unit, so as to obtain the object allocation operation output data of the data corresponding to each combination in the target inspection unit; in addition, the object allocation operation output data between the target inspection units having the target cross relationship may mean that the decimated typical air cleaning devices in the sub-typical device combination a corresponding to the 1 st target inspection unit are present in each sub-typical device combination of the 2 nd target inspection unit, the decimated typical air cleaning devices in the sub-typical device combination B corresponding to the 1 st target inspection unit are also present in each sub-typical device combination of the 2 nd target inspection unit;
Based on the object distribution operation output data of each combination corresponding data in the target inspection unit, each combination corresponding data is respectively used as a manual checking object, so as to perform unit-division comparison detection operation according to the target inspection unit, and output grouping evaluation operation output data of each combination corresponding data, namely, manually checking and evaluating state parameters, wherein the state parameters can be richness and/or integrity of description data, the description data can refer to device operation description data or other data, at least one typical air purifying device can be included in a comparison typical device combination, and the typical air purifying device in the comparison typical device combination can be a typical air purifying device matched with a target operation state, so that the grouping evaluation operation output data of the comparison typical device combination can be used as reference grouping evaluation operation output data;
and, in the analyzed combination correspondence data, a decimated representative air cleaning device included in the representative device combination is marked as a sampled representative air cleaning device, for example, a decimated representative air cleaning device included in a representative device combination having a corresponding state parameter greater than that of the comparative representative device combination may be used as the sampled representative air cleaning device.
It should be understood that, in some possible embodiments, the step S130 may further include the following specific implementations:
determining historical device description information of the sampling typical air purification device;
and according to the historical device description information, performing key information screening operation on the sampling typical air purification device to obtain device operation description data comprising description data of device key information, description data of user key information and description data of operation key information, wherein the device key information is used for representing attribute information (such as various indexes and the like) of the sampling typical air purification device, the user key information is used for representing attribute information (identity-related and habit-related information) of a user used by a device corresponding to the sampling typical air purification device, and the operation key information is used for representing operation process information of the sampling typical air purification device.
It should be understood that, in some possible embodiments, the step S140 may further include the following specific implementations:
analyzing a data mining manner corresponding to the data type information of each local device operation description data in the device operation description data, where the local device operation description data may refer to description data of the device key information, description data of the user key information, or description data of the operation key information, or may be divided based on other manners to form local device operation description data, which will be described later;
According to the data mining mode corresponding to each local device operation description data, carrying out data characteristic mining operation on each local device operation description data so as to mine out a local device operation characteristic representation corresponding to each local device operation description data;
and performing aggregation operation of the characteristic representation according to the local device operation characteristic representation to determine the device operation characteristic representation corresponding to the sampling typical air purification device, wherein the aggregation operation of the characteristic representation at least comprises cascade combination operation of the local device operation characteristic representation.
It should be understood that, in some possible embodiments, the mined local device operation feature representation includes a first local device operation feature representation and a second local device operation feature representation, based on which the step of performing a data feature mining operation on each of the local device operation description data according to a data mining manner corresponding to each of the local device operation description data to mine out the local device operation feature representation corresponding to each of the local device operation description data may further include the following specific implementation matters:
Performing a comparative analysis operation on each of the local device operation description data to form local device operation description data satisfying the abnormal state analysis and local device operation description data not satisfying the abnormal state analysis, wherein the local device operation description data satisfying the abnormal state analysis may be complete local device operation description data, and the local device operation description data not satisfying the abnormal state analysis may refer to incomplete local device operation description data, such as partial data having a loss, for example, description data of complete device key information, may be used as local device operation description data satisfying the abnormal state analysis, and description data of incomplete device key information may be used as local device operation description data not satisfying the abnormal state analysis;
performing data feature mining operation on the local device operation description data meeting the abnormal state analysis according to a corresponding data mining mode to output a first local device operation feature representation corresponding to the local device operation description data meeting the abnormal state analysis, namely directly performing feature mining on the local device operation description data meeting the abnormal state analysis, for example, by convolution;
And performing an abnormal data embedding operation on the local device operation description data which does not meet the abnormal state analysis, and marking the output abnormal data embedding feature representation as a second local device operation feature representation corresponding to the local device operation description data which does not meet the abnormal state analysis, for example, the embedding operation can be performed on the distribution coordinates of the identifier and the lost data for representing the abnormal state analysis while performing feature mining on the local device operation description data which does not meet the abnormal state analysis, so that the result of the feature mining operation and the result of the embedding operation can be combined, thereby obtaining the corresponding second local device operation feature representation.
It should be understood that, in some possible embodiments, the method for analyzing an abnormality of the air purification control system may further include the following specific implementation matters:
for the two target air cleaning devices having correlation, the first target air cleaning device and the second target air cleaning device are respectively marked, and for example, any one of the two target air cleaning devices having correlation may be marked as the first target air cleaning device, and the other one is marked as the second target air cleaning device;
Acquiring device operation description data of the first target air purification device to form first device operation description data, and acquiring device operation description data of the second target air purification device to form second device operation description data;
mining description data characteristic representations corresponding to the first device operation description data and the second device operation description data respectively, wherein the description data characteristic representations are used for reflecting each description data segment in the corresponding device operation description data and the distribution coordinates of each description data segment in the corresponding device operation description data;
performing focusing characteristic analysis strengthening operation of the sub-channels on each description data characteristic representation respectively so as to output strengthening data characteristic representations corresponding to each description data characteristic representation respectively;
performing associated focusing characteristic analysis operation on each enhanced data characteristic representation to form a first data focusing characteristic representation corresponding to the first device operation description data and a second data focusing characteristic representation corresponding to the second device operation description data;
performing a data correlation analysis operation on the first device operation description data and the second device operation description data according to the first data focusing characteristic representation and the second data focusing characteristic representation to output corresponding data correlation characterization parameters, for example, the data correlation characterization parameters can be obtained by calculating vector cosine similarity between the first data focusing characteristic representation and the second data focusing characteristic representation, so as to obtain the data correlation characterization parameters between the first device operation description data and the second device operation description data;
Based on the data correlation characterization parameters, performing reliability analysis operation on the abnormal analysis result data corresponding to the two target air purification devices with correlation to output reliability analysis results corresponding to the two target air purification devices with correlation, where the reliability analysis results are used to reflect the reliability degree of the abnormal analysis result data corresponding to the two target air purification devices with correlation, for example, performing similarity analysis on the abnormal analysis result data corresponding to the two target air purification devices with correlation to obtain corresponding result similarity, and then performing comparison analysis on the result similarity and the data correlation characterization parameters to determine corresponding reliability analysis results, for example, the smaller the difference between the result similarity and the data correlation characterization parameters is, the larger the corresponding reliability degree is, and conversely, the larger the difference between the result similarity and the data correlation characterization parameters is, the smaller the corresponding reliability degree is.
It should be understood that, in some possible embodiments, the step of mining the description data feature representations corresponding to the first device operation description data and the second device operation description data respectively may further include the following specific implementation matters:
Mining primary data characteristic representations corresponding to the first device operation description data and the second device operation description data, for example, convolution processing can be performed on the first device operation description data and the second device operation description data respectively; for each primary data characteristic representation, analyzing a distribution coordinate characteristic representation corresponding to the distribution coordinates of the target data units in each description data segment according to each description data segment corresponding to the primary data characteristic representation; and merging the distributed coordinate feature representation into the corresponding primary data feature representation to form a description data feature representation for each of the first device operation description data and the second device operation description data, e.g., the distributed coordinate feature representation and the corresponding primary data feature representation may be subjected to a cascading combination operation to form a corresponding description data feature representation.
It should be understood that, in some possible embodiments, the step of analyzing, for each primary data feature representation, a distribution coordinate feature representation corresponding to a distribution coordinate of a target data unit in each description data segment according to each description data segment corresponding to the primary data feature representation may further include the following specific implementation matters:
For each primary data characteristic representation, analyzing the distribution position information of a target data unit in each description data fragment in the corresponding device operation description data according to each description data fragment corresponding to the primary data characteristic representation, wherein the target data unit can be a target keyword or a target key sentence, and can be determined based on a preset data unit set;
each of the distribution position information is subjected to a feature space mapping operation (implemented by convolution) based on the feature representation dimension number of the primary data feature representation to form a distribution coordinate feature representation having the same feature representation dimension number as the primary data feature representation.
It should be understood that, in some possible embodiments, the step of performing the focus feature analysis enhancement operation for each of the description data feature representations to output the enhancement data feature representations corresponding to each of the description data feature representations, may further include the following specific implementation matters:
for each device operation description data, determining a description data characteristic representation corresponding to the device operation description data and a channel local characteristic representation associated with the description data characteristic representation; and carrying out multiplication operation on each description data characteristic representation and the associated channel local characteristic representation so as to output an enhanced data characteristic representation corresponding to each device operation description data.
It should be understood that, in some possible embodiments, the step of determining, for each device operation description data, that the description data feature corresponding to the device operation description data represents the associated channel local feature representation may further include the following specific implementation matters:
for each device operation description data, performing pooling operation on the description data characteristic representation based on the characteristic representation dimension of the description data characteristic representation of the device operation description data to form pooled characteristic representations corresponding to the device operation description data, for example, for each characteristic representation dimension, determining a mean value or a maximum value of each characteristic representation parameter included in the characteristic representation dimension, and then combining the mean value or the maximum value corresponding to the characteristic representation dimension to form a corresponding pooled characteristic representation;
performing a data conversion operation and an activation operation on the pooled feature representation to output a channel local feature representation associated with the descriptive data feature representation, the data conversion operation may include weighting the pooled feature representation based on a first parameter included in the respective neural network and then summing the weighted results based on a second parameter included in the respective neural network, the activation operation may include processing the results of the data conversion operation by a configured activation function.
It should be appreciated that, in some possible embodiments, the step of performing the focus feature analysis operation for associating each of the enhanced data feature representations to form a first data focus feature representation corresponding to the first device operation description data and a second data focus feature representation corresponding to the second device operation description data may further include the following specific implementation matters:
performing a cascade combination operation on the enhanced data feature representation corresponding to the first device operation description data and the enhanced data feature representation corresponding to the second device operation description data to form a corresponding cascade enhanced data feature representation, for example, the cascade enhanced data feature representation may be { the enhanced data feature representation corresponding to the first device operation description data, the enhanced data feature representation corresponding to the second device operation description data }; performing focusing characteristic analysis operation on the cascade enhancement data characteristic representation to output a corresponding focusing enhancement data characteristic representation; and according to the cascade combination principle of the cascade enhancement data characteristic representation, performing a decomposition operation of the characteristic representation on the focus enhancement data characteristic representation, namely performing an operation opposite to the cascade combination operation, so as to form a first data focus characteristic representation corresponding to the first device operation description data and a second data focus characteristic representation corresponding to the second device operation description data.
Wherein, it should be understood that, in some possible embodiments, the step of performing the focus characteristic analysis operation on the cascade enhancement data characteristic representation to output a corresponding focus enhancement data characteristic representation may further include the following specific implementation matters:
according to the cascade enhancement data characteristic representation, analyzing a to-be-processed characteristic representation of a focusing characteristic analysis unit and an influence characteristic representation for analyzing focusing influence parameters;
and loading the feature representation to be processed and the influence feature representation to load the feature representation to be processed and the influence feature representation to the focusing feature analysis unit for multiplication operation, and taking the analysis result of the focusing feature analysis unit as the focusing enhancement data feature representation.
It should be understood, that, in some possible embodiments, the step of analyzing the pending feature representation of the focus feature analysis unit and the influence feature representation for analyzing the focus influence parameter according to the cascade enhancement data feature representation may further include the following specific implementation matters:
based on the first mapping parameter distribution, the second mapping parameter distribution and the third mapping parameter distribution included in the corresponding neural network, performing multiplication operation on the cascade enhancement data feature representation respectively to output a corresponding first mapping feature representation, second mapping feature representation and third mapping feature representation;
And multiplying the transposed data of the second mapping feature representation and the third mapping feature representation, and taking the multiplication result as the influence feature representation.
Referring to fig. 3, an embodiment of the present invention further provides an abnormality analysis device of an air purification control system, which is applicable to the abnormality analysis system of the air purification control system. Wherein the abnormality analysis device of the air purification control system (the abnormality analysis device belongs to a software virtual device) comprises:
the air purification device lottery module is used for carrying out lottery operation on typical air purification devices included in the typical purification device cluster according to at least one lottery rule so as to determine lottery typical air purification devices matched with a lottery state of the target device;
the air purification device sampling module is used for performing sampling operation on the sampling typical air purification device through grouping evaluation operation so as to output sampling typical air purification devices corresponding to grouping evaluation operation output data matched with a target running state;
the description data analysis module is used for determining device operation description data of the sampling typical air purification device and analyzing a corresponding data mining mode based on data type information of the device operation description data, wherein the device operation description data belongs to text data;
The description data mining module is used for performing data characteristic mining operation on the device operation description data based on the data mining mode so as to output device operation characteristic representations corresponding to the sampling typical air purifying device;
the anomaly analysis network optimization module is used for carrying out network optimization operation on an initial anomaly analysis network through the device operation characteristic representation so as to output a target anomaly analysis network corresponding to the initial anomaly analysis network;
the device anomaly analysis module is used for determining the residual using time of the filter device in the target air purification device based on the current power of the fan in the target air purification device, determining the size relation between the residual using time and the preset time, and carrying out anomaly analysis operation on the target air purification device through the target anomaly analysis network under the condition that the residual using time is less than or equal to the preset time so as to output anomaly analysis result data corresponding to the target air purification device, wherein the anomaly analysis result data is used for reflecting the operation anomaly type of the target air purification device.
In summary, the abnormality analysis method and system for an air purification control system provided by the present invention may determine a typical air purification device to be selected from typical air purification devices included in a typical purification device cluster; determining a sampling typical air purifying device; determining device operation description data of a sampling typical air purifying device, and analyzing a corresponding data mining mode; performing data feature mining operation on the device operation description data based on a data mining mode to output device operation feature representation; performing network optimization operation on the initial anomaly analysis network through device operation feature representation to output a target anomaly analysis network; and performing an anomaly analysis operation on the target air purification device through the target anomaly analysis network to output anomaly analysis result data corresponding to the target air purification device. Based on the foregoing, the device operation characteristics for performing the network optimization operation indicate that the corresponding device operation description data is obtained through two times of screening, so that the basis of the network optimization operation is more reliable, and therefore, the analysis precision of the target anomaly analysis network formed by optimization can be ensured, the reliability of the anomaly analysis result data analyzed based on the target anomaly analysis network is ensured, that is, the reliability of anomaly analysis is improved to a certain extent, and the problem of low reliability in the prior art is solved.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An abnormality analysis method of an air purification control system, comprising:
performing a lottery operation on typical air cleaning devices included in the typical cleaning device cluster according to at least one lottery rule to determine a lottery typical air cleaning device matching a lottery state of the target device;
sampling operation is carried out on the sampling typical air purifying device through grouping evaluation operation, so that sampling typical air purifying devices corresponding to grouping evaluation operation output data matched with a target running state are output;
determining device operation description data of the sampling typical air purification device, and analyzing a corresponding data mining mode based on data type information of the device operation description data, wherein the device operation description data belongs to text data;
performing data characteristic mining operation on the device operation description data based on the data mining mode to output device operation characteristic representations corresponding to the sampling typical air purifying device;
Performing network optimization operation on an initial anomaly analysis network through the device operation characteristic representation to output a target anomaly analysis network corresponding to the initial anomaly analysis network;
determining the residual using time of a filter device in a target air purifying device based on the current power of a fan in the target air purifying device, determining the size relation between the residual using time and a preset time, and carrying out an abnormality analysis operation on the target air purifying device through the target abnormality analysis network under the condition that the residual using time is less than or equal to the preset time so as to output abnormality analysis result data corresponding to the target air purifying device, wherein the abnormality analysis result data is used for reflecting the abnormal operation type of the target air purifying device.
2. The abnormality analysis method of an air cleaning control system according to claim 1, wherein the step of performing a lottery operation on typical air cleaning devices included in a typical cleaning device cluster according to at least one lottery rule to determine a lottery typical air cleaning device matching a lottery state of a target device, comprises:
Marking a reference typical air purification device in the typical air purification devices included in the typical purification device cluster, wherein the reference typical air purification device is provided with a corresponding manual operation identifier;
performing a device-associated replenishment operation based on the reference typical air cleaning device such that, in the typical cleaning device cluster, a replenishment typical air cleaning device associated with the reference typical air cleaning device is decimated;
the reference representative air cleaning device and the supplemental representative air cleaning device are marked to mark as a lottery representative air cleaning device matching a lottery status of the target device.
3. The abnormality analysis method of an air cleaning control system according to claim 2, wherein the supplementary typical air cleaning device belongs to a combination result of a first typical air cleaning device and a second typical air cleaning device;
the step of performing a device-associated replenishment operation based on the reference typical air cleaning device such that, in the typical cleaning device cluster, a replenishment typical air cleaning device associated with the reference typical air cleaning device is decimated, comprising:
performing a contrast analysis operation on a specified typical air cleaning device to output a first typical air cleaning device associated with the reference typical air cleaning device among the specified typical air cleaning devices including a portion of the reference typical air cleaning device and typical air cleaning devices other than the reference typical air cleaning device in the typical cleaning device cluster by an optimized contrast analysis model formed by performing a network optimization operation on a portion of the reference typical air cleaning device as relevant typical data and the specified typical air cleaning device as irrelevant typical data;
Performing a related device supplementing operation according to the reference typical air purifying device to determine a second typical air purifying device associated with the reference typical air purifying device in the typical air purifying devices included in the typical air purifying device cluster.
4. The abnormality analysis method of an air purification control system as recited in claim 3, wherein the abnormality analysis method of an air purification control system further includes:
performing a device analysis operation on candidate typical air cleaning devices through a device association analysis model to analyze a third typical air cleaning device associated with the reference typical air cleaning device among the candidate typical air cleaning devices, the candidate typical air cleaning devices including typical air cleaning devices other than the reference typical air cleaning device in the typical air cleaning device cluster;
classifying the candidate typical air cleaning devices so as to analyze a fourth typical air cleaning device associated with the reference typical air cleaning device among the candidate typical air cleaning devices;
the reference typical air cleaning device, the supplementary typical air cleaning device, the third typical air cleaning device, and the fourth typical air cleaning device are combined to form a lottery typical air cleaning device matching a lottery state of a target device.
5. The abnormality analysis method of an air cleaning control system according to claim 1, wherein said step of determining device operation description data of said sampled representative air cleaning device includes:
determining historical device description information of the sampling typical air purification device;
and according to the historical device description information, performing key information screening operation on the sampling typical air purification device to obtain device operation description data comprising description data of device key information, description data of user key information and description data of operation key information, wherein the device key information is used for representing attribute information of the sampling typical air purification device, the user key information is used for representing attribute information of a user used by a device corresponding to the sampling typical air purification device, and the operation key information is used for representing operation process information of the sampling typical air purification device.
6. The abnormality analysis method of an air cleaning control system according to claim 1, wherein the step of performing a data feature mining operation on the device operation description data based on the data mining means to output a device operation feature representation corresponding to the sampled typical air cleaning device includes:
Analyzing a data mining mode corresponding to data type information of each local device operation description data in the device operation description data;
according to the data mining mode corresponding to each local device operation description data, carrying out data characteristic mining operation on each local device operation description data so as to mine out a local device operation characteristic representation corresponding to each local device operation description data;
and performing aggregation operation of the characteristic representation according to the local device operation characteristic representation to determine the device operation characteristic representation corresponding to the sampling typical air purification device, wherein the aggregation operation of the characteristic representation at least comprises cascade combination operation of the local device operation characteristic representation.
7. The abnormality analysis method of an air cleaning control system according to claim 6, wherein the mined partial device operation characteristic representation includes a first partial device operation characteristic representation and a second partial device operation characteristic representation;
the step of performing data feature mining operation on each local device operation description data according to a data mining mode corresponding to each local device operation description data so as to mine out a local device operation feature representation corresponding to each local device operation description data includes:
Performing a comparative analysis operation on each of the local device operation description data to form local device operation description data satisfying the abnormal state analysis and local device operation description data not satisfying the abnormal state analysis;
performing data feature mining operation on the local device operation description data meeting the abnormal state analysis according to a corresponding data mining mode to output a first local device operation feature representation corresponding to the local device operation description data meeting the abnormal state analysis;
and performing abnormal data embedding operation on the local device operation description data which does not meet the abnormal state analysis, and marking the output abnormal data embedding characteristic representation as a second local device operation characteristic representation corresponding to the local device operation description data which does not meet the abnormal state analysis.
8. The abnormality analysis method of an air purification control system according to any one of claims 1 to 7, characterized in that the abnormality analysis method of an air purification control system further comprises:
for two target air cleaning devices having correlation, the first target air cleaning device and the second target air cleaning device are respectively marked;
Acquiring device operation description data of the first target air purification device to form first device operation description data, and acquiring device operation description data of the second target air purification device to form second device operation description data;
mining description data characteristic representations corresponding to the first device operation description data and the second device operation description data respectively, wherein the description data characteristic representations are used for reflecting each description data segment in the corresponding device operation description data and the distribution coordinates of each description data segment in the corresponding device operation description data;
performing focusing characteristic analysis strengthening operation of the sub-channels on each description data characteristic representation respectively so as to output strengthening data characteristic representations corresponding to each description data characteristic representation respectively;
performing associated focusing characteristic analysis operation on each enhanced data characteristic representation to form a first data focusing characteristic representation corresponding to the first device operation description data and a second data focusing characteristic representation corresponding to the second device operation description data;
performing data correlation analysis operation on the first device operation description data and the second device operation description data according to the first data focusing characteristic representation and the second data focusing characteristic representation so as to output corresponding data correlation characterization parameters;
And based on the data correlation characterization parameters, performing reliability analysis operation on the abnormal analysis result data corresponding to the two target air purification devices with correlation to output reliability analysis results corresponding to the two target air purification devices with correlation, wherein the reliability analysis results are used for reflecting the reliability degree of the abnormal analysis result data corresponding to the two target air purification devices with correlation.
9. The abnormality analysis method of an air cleaning control system according to claim 8, wherein the step of mining out the description data characteristic representations respectively corresponding to the first device operation description data and the second device operation description data includes:
mining primary data characteristic representations corresponding to the first device operation description data and the second device operation description data respectively; for each primary data characteristic representation, analyzing a distribution coordinate characteristic representation corresponding to the distribution coordinates of the target data units in each description data segment according to each description data segment corresponding to the primary data characteristic representation; and merging the distributed coordinate feature representations into the corresponding primary data feature representations to form description data feature representations respectively corresponding to the first device operation description data and the second device operation description data;
And the step of performing focus feature analysis enhancement operation of the channels on each of the description data feature representations to output enhancement data feature representations corresponding to each of the description data feature representations, includes:
for each device operation description data, determining a description data characteristic representation corresponding to the device operation description data and a channel local characteristic representation associated with the description data characteristic representation; and performing multiplication operation on each description data characteristic representation and the associated channel local characteristic representation to output an enhanced data characteristic representation corresponding to each device operation description data;
and performing a focus characteristic analysis operation associated with each of the enhanced data characteristic representations to form a first data focus characteristic representation corresponding to the first device operation description data and a second data focus characteristic representation corresponding to the second device operation description data, including:
performing cascading combination operation on the reinforced data characteristic representation corresponding to the first device operation description data and the reinforced data characteristic representation corresponding to the second device operation description data to form a corresponding cascading reinforced data characteristic representation; performing focusing characteristic analysis operation on the cascade enhancement data characteristic representation to output a corresponding focusing enhancement data characteristic representation; and performing a decomposition operation of the feature representation on the focus reinforcement data feature representation according to a cascade combination principle of the cascade reinforcement data feature representation to form a first data focus feature representation corresponding to the first device operation description data and a second data focus feature representation corresponding to the second device operation description data.
10. An anomaly analysis system of an air cleaning control system, comprising a processor and a memory, the memory for storing a computer program, the processor for executing the computer program to implement the method of any one of claims 1-9.
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| CN117197739A (en) * | 2023-09-08 | 2023-12-08 | 江苏平熙智能电子科技有限公司 | Monitoring data processing method and system for intelligent building |
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